How wonderful it is, in our technologically advanced society, that we can now integrate data across various platforms and applications!

That is- until we have to reconcile conflicting information. Is the latest entry the most accurate? Has the same customer been entered into the system twice with slight name variations?

According to AmericanBanker.Com, banks plan to spend an average of 5.7% more on data related technologies including data warehousing, mining, and online analytical processing- second only to a 6.8% increase in budgets for online and mobile banking improvements.

In recent years, productivity through streamlined data and the vital importance of data integrity have been burned into the pages of a million business strategies. Data is easily gathered and stored by thousands of applications across millions of devices.

The problem? It still takes sentient intelligence to make data-based decisions. How is an algorithm to know which recorded client file is accurate without calling the customer?

Automated data integration causes duplicate entries, inconsistencies, inaccuracies and confusion across the business. Misspellings of names, alternate spellings of names… any number of human errors can contribute to these problems, which an in-house algorithm has little to no chance of integrating properly across platforms.

So what do we do? It’s obvious we need to integrate our records, but how do we reconcile the varying and conflicting records?

Rather than working to gather and integrate the information from each of your silo’ed departments, a business can instead purchase the data from a highly trusted, large database of US and Worldwide businesses in a format that fits your specific needs, using the most basic of initial data available.

It may not be the newest or fanciest concept in big data cyberspace, but it sure is the simplest way to employ the power of big data without employing the big data analysts.

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